https://www.lef.wiwi.uni-due.de/fileadmin/fileupload/BWL-LEF/Seminarreihe/RWeron14_Essen.pdf

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.

This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported.

Day-ahead electricity price forecasting using the wavelet transform and ARIMA models

Day-ahead ,melectricity price forecasting using the wavelet transform and arima models

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

Optimal sizing of hybrid PV/diesel/battery in ship power system ☆

Determination of feeder configuration involving the switch statuses is important for the operation in the distribution system. Minimization of the megawatt loss is crucial if the system is in a normal condition; the voltage drop is expected to be as low as possible while a fault occurs in the system. This paper presents a method based on genetic algorithms (GAs) to determine the network configuration. The proposed method, formulated as a fuzzy multiobjective problem, takes both the normal condition and the contingencies (faults) into account. The switch statuses are considered as preventive controls for multiple individual balanced faults. A vertex encoding based on the Prufer number is used in GA for encoding the chromosomes (strings). The Prufer number ensures that the system structure will be radial for the distribution system. The simulation results from two distribution systems are used to show the applicability of the proposed method.

Determination of network configuration considering multiobjective in distribution systems using genetic algorithms

Owing to the Kyoto Protocol and the growing depletion of natural resources, renewable energies have attracted much attention. This paper considers 25-kW wind-turbine generator, 5-kW PV and 30-kW diesel generator as unit sizes for generation planning in a stand-alone power system. The investment cost (installation and unit costs) and fuel cost are minimized while retaining the reliability requirement and CO emission limit. First, the fuzzy-c-means (FCM) is employed to cluster the operation states for system load, wind-turbine generations (WTG), and PV in 8760 h. Then, the Markov models for the system load, WTG, and photovoltaic (PV) are established. The Markov models are embedded into the genetic algorithm to determine the optimal sizes for WTG, PV, and the diesel generator. The simulation results reveal that computation time can be reduced greatly while optimality can be still retained, compared with the traditional method using chronological data.

Optimal Sizing of Hybrid Wind/PV/Diesel Generation in a Stand-Alone Power System Using Markov-Based Genetic Algorithm

A hybrid deep learning-based neural network for 24-h ahead wind power forecasting

Bidding competition is one of the main transaction approaches in deregulated electricity markets. Locational marginal prices (LMPs) resulting from bidding competition represent electricity values at nodes or in areas. A method using both neural networks (NNs) and fuzzy-c-means (FCM) is presented for forecasting LMPs. The recurrent neural network (RNN) was addressed and the traditional NN-based on a backpropagation algorithm was also investigated for comparison. The FCM helped classify the load levels into three clusters. Individual RNNs according to three load clusters were developed for forecasting LMPs. These RNNs were trained/ validated and tested with historical data from the PJM (Pennsylvania, New Jersey, and Maryland) power system. It was found that the proposed neural networks were capable of forecasting LMP values efficiently.

Ying-Yi Hong

Papers

Optimal sizing of hybrid PV/diesel/battery in ship power system ☆

Determination of network configuration considering multiobjective in distribution systems using genetic algorithms

Optimal Sizing of Hybrid Wind/PV/Diesel Generation in a Stand-Alone Power System Using Markov-Based Genetic Algorithm

A hybrid deep learning-based neural network for 24-h ahead wind power forecasting

Locational marginal price forecasting in deregulated electricity markets using artificial intelligence